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Space shuttles

Electroadhesive robotics experiment in simulated microgravity

June 9, 2023 by Makenna Kuzyk

Canada has a strong presence in space robotics, and Mission SpaceWalker (MSW) is a group of ambitious young women who are leaning into Canadian robotics excellence through their investigation of how electroadhesive (EA) robots behave in reduced gravity, all while introducing a new testing procedure for rovers and adhesive space robotics. As an undergraduate student team at the … [Read more...] about Electroadhesive robotics experiment in simulated microgravity

Testing satellite control systems with drones

January 1, 2022 by Ali Barari, Ryan Dion, Ian Jeffrey and Philip Ferguson

Image of a cube in space.

  In space, it is usually not possible to repair equipment when things go wrong. While astronauts ventured into space aboard the Space Shuttle to repair the infamously flawed Hubble Space Telescope, these types of repair missions are extremely expensive and rare. Limited repair options make spacecraft engineers turn their attention to reliability in an attempt to … [Read more...] about Testing satellite control systems with drones

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About the Magazine

IEEE Potentials Magazine is the publication dedicated to undergraduate and graduate students and young professionals. IEEE Potentials explores career strategies, the latest in research, and important technical developments. Through its articles, it also relates theories to practical applications, highlights technology’s global impact, and generates international forums that foster the sharing of diverse ideas about the profession.

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Privacy-preserving distributed learning: Techniques, applications, and future challenges

The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.

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